RemoteSensingToolbox

RemoteSensingToolbox is a pure Julia package built on top of Rasters.jl for reading, visualizing, and processing remotely sensed imagery. Users may refer to the Tutorials section in the docs for examples on how to use this package.

Installation

To install this package, first start the Julia REPL and then open the package manager by typing ]. You can then download RemoteSensingToolbox directly from the official Julia repository like so:

(@v1.9) pkg> add RemoteSensingToolbox

Once RemoteSensingToolbox has been installed, you can import it like any other Julia package. Please note that many features require you to also import the Rasters package.

using RemoteSensingToolbox, Rasters

Features

This package is a work in progress, which means that new features are being added and existing features are subject to change. To contribute, please create an issue on GitHub or open a pull request. A summary of both existing and planned features is provided below:

Feature	Description	Implemented
Reading and Writing	Read layers from a scene and the write results to disk	✅
Visualization	Visualize images with various band composites	✅
Land Cover Indices	Calculate indices such as MNDWI and NDVI	✅
QA and SCL Decoding	Decode Quality Assurance and Scene Classification masks	✅
Pixel Masking	Mask pixels to remove objects such as clouds or shadows	✅
PCA	Perform PCA analysis, transformation, and reconstruction	✅
MNF	Minimum Noise Fraction transformation and reconstruction	✅
Signature Analysis	Visualize spectral signatures for different land cover types	✅
Land Cover Classification	Exposes an MLJ interface for classifying land cover types	❌
Endmember Extraction	Extract spectral endmembers from an image	❌
Spectral Unmixing	Perform spectral unmixing under a given endmember library	❌

Rasters.jl

RemoteSensingToolbox is intended to be used in conjunction with the wider Julia ecosystem and as such, seeks to avoid duplicating functinalities provided by other packages. As the majority of methods accept and return AbstractRaster or AbstractRasterStack objects, users should be able to call methods from Rasters.jl at any point in the processing pipeline. A summary of common functionalities offered by Rasters.jl is provided below:

Method	Description
mosaic	Join rasters covering different extents into a single array or file.
crop	Shrink objects to specific dimension sizes or the extent of another object.
extend	Extend objects to specific dimension sizes or the extent of another object.
trim	Trims areas of missing values for arrays and across stack layers.
resample	Resample data to a different size and projection, or snap to another object.
mask	Mask a raster by a polygon or the non-missing values of another Raster.
replace_missing	Replace all missing values in a raster and update missingval.
extract	Extract raster values from points or geometries.
zonal	Calculate zonal statistics for a raster masked by geometries.

Quickstart Example

Typically, the first step in a workflow is to read the desired layers from disk. To do so, we first need to place our product within the appropriate context; in this case Landsat8. With this done, we can load whichever layers we desire by simply requesting them by name. A complete list of all available layers can be acquired by calling layers(Landsat8). We typically use a Raster to load a single layer, while a RasterStack is used to load multiple layers at once. By default, RasterStack will read all of the band layers when none are specified. We can also set lazy=true to avoid reading everything into memory up-front.

using RemoteSensingToolbox, Rasters

# Read Landsat Bands
src = Landsat8("data/LC08_L2SP_043024_20200802_20200914_02_T1")
stack = RasterStack(src, lazy=true)

Now let's visualize our data to see what we're working with. The true_color method uses the red, green, and blue bands to produce an image that is familiar to the human eye. In most other frameworks, we would have to specify each of these bands individually, which in turn requires knowledge about the sensor in question. However, because we have placed our scene within a Landsat8 context, true_color is smart enough to figure this out on its own. As an alternative, we could have also called true_color(Landsat8, stack; upper=0.90), which requires passing in the sensor type as the first agument and a RasterStack containing the required bands as the second. Many other methods in RemoteSensingToolbox follow this same pattern.

true_color(src; upper=0.90)

You may have noticed that we set the keyword upper to 0.90. This parameter defines the upper quantile that is used during histogram adjustment and is set to 0.98 by default. However, the presence of bright clouds requires us to lower it in order to prevent the image from appearing too dark. We can remove these clouds by passing the cloud and cloud shadow masks into the apply_masks method. As with other layers, we can simply request them by name.

# Mask Clouds
cloud_mask = Raster(src, :clouds)
shadow_mask = Raster(src, :cloud_shadow)
masked = apply_masks(stack, cloud_mask, shadow_mask)

# Visualize in True Color
true_color(Landsat8, masked)

Now let's try to visualize some other band combinations. The Agriculture band combination is commonly used to emphasize regions with healthy vegetation, which appear as various shades of green.

agriculture(src; upper=0.90)

We'll finish this example by computing a few different land cover indices. Each index expects two bands as input, such as green and swir (MNDWI), red and nir (NDVI), or nir and swir (NDMI). As with visualization, we do not need to specify these bands manually so long as the sensor type is known. In general, each index has three forms: one that requires only a single AbstractSatellite instance, a second that expects both the type of satellite and a RasterStack, and a third which expects a Raster for each band.

# Extract Region of Interest
roi = @view masked[X(5800:6800), Y(2200:3200)]

# Calculate Indices
indices = map(visualize, [mndwi(Landsat8, roi), ndvi(Landsat8, roi), ndmi(Landsat8, roi)])

# Visualize
tc = true_color(Landsat8, roi; upper=0.998)
mosaic = mosaicview([tc, indices...]; npad=10, fillvalue=0.0, ncol=2, rowmajor=true)

For more examples, refer to the docs.